Update config.py

config.py

@@ -1,218 +1,218 @@
-import os
-import re
-import sys
-
-import torch
-
-from tools.i18n.i18n import I18nAuto
-
-i18n = I18nAuto(language=os.environ.get("language", "Auto"))
-
-
-pretrained_sovits_name = {
-    "v1": "pretrained_models/s2G488k.pth",
-    "v2": "pretrained_models/gsv-v2final-pretrained/s2G2333k.pth",
-    "v3": "pretrained_models/s2Gv3.pth",  ###v3v4还要检查vocoder，算了。。。
-    "v4": "pretrained_models/gsv-v4-pretrained/s2Gv4.pth",
-    "v2Pro": "pretrained_models/v2Pro/s2Gv2Pro.pth",
-    "v2ProPlus": "pretrained_models/v2Pro/s2Gv2ProPlus.pth",
-}
-
-pretrained_gpt_name = {
-    "v1": "pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt",
-    "v2": "pretrained_models/gsv-v2final-pretrained/s1bert25hz-5kh-longer-epoch=12-step=369668.ckpt",
-    "v3": "pretrained_models/s1v3.ckpt",
-    "v4": "pretrained_models/s1v3.ckpt",
-    "v2Pro": "pretrained_models/s1v3.ckpt",
-    "v2ProPlus": "pretrained_models/s1v3.ckpt",
-}
-name2sovits_path = {
-    # i18n("不训练直接推v1底模！"): "pretrained_models/s2G488k.pth",
-    i18n("不训练直接推v2底模！"): "pretrained_models/gsv-v2final-pretrained/s2G2333k.pth",
-    # i18n("不训练直接推v3底模！"): "pretrained_models/s2Gv3.pth",
-    # i18n("不训练直接推v4底模！"): "pretrained_models/gsv-v4-pretrained/s2Gv4.pth",
-    i18n("不训练直接推v2Pro底模！"): "pretrained_models/v2Pro/s2Gv2Pro.pth",
-    i18n("不训练直接推v2ProPlus底模！"): "pretrained_models/v2Pro/s2Gv2ProPlus.pth",
-}
-name2gpt_path = {
-    # i18n("不训练直接推v1底模！"):"pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt",
-    i18n(
-        "不训练直接推v2底模！"
-    ): "pretrained_models/gsv-v2final-pretrained/s1bert25hz-5kh-longer-epoch=12-step=369668.ckpt",
-    i18n("不训练直接推v3底模！"): "pretrained_models/s1v3.ckpt",
-}
-SoVITS_weight_root = [
-    "SoVITS_weights",
-    "SoVITS_weights_v2",
-    "SoVITS_weights_v3",
-    "SoVITS_weights_v4",
-    "SoVITS_weights_v2Pro",
-    "SoVITS_weights_v2ProPlus",
-]
-GPT_weight_root = [
-    "GPT_weights",
-    "GPT_weights_v2",
-    "GPT_weights_v3",
-    "GPT_weights_v4",
-    "GPT_weights_v2Pro",
-    "GPT_weights_v2ProPlus",
-]
-SoVITS_weight_version2root = {
-    "v1": "SoVITS_weights",
-    "v2": "SoVITS_weights_v2",
-    "v3": "SoVITS_weights_v3",
-    "v4": "SoVITS_weights_v4",
-    "v2Pro": "SoVITS_weights_v2Pro",
-    "v2ProPlus": "SoVITS_weights_v2ProPlus",
-}
-GPT_weight_version2root = {
-    "v1": "GPT_weights",
-    "v2": "GPT_weights_v2",
-    "v3": "GPT_weights_v3",
-    "v4": "GPT_weights_v4",
-    "v2Pro": "GPT_weights_v2Pro",
-    "v2ProPlus": "GPT_weights_v2ProPlus",
-}
-
-
-def custom_sort_key(s):
-    # 使用正则表达式提取字符串中的数字部分和非数字部分
-    parts = re.split("(\d+)", s)
-    # 将数字部分转换为整数，非数字部分保持不变
-    parts = [int(part) if part.isdigit() else part for part in parts]
-    return parts
-
-
-def get_weights_names():
-    SoVITS_names = []
-    for key in name2sovits_path:
-        if os.path.exists(name2sovits_path[key]):
-            SoVITS_names.append(key)
-    for path in SoVITS_weight_root:
-        if not os.path.exists(path):
-            continue
-        for name in os.listdir(path):
-            if name.endswith(".pth"):
-                SoVITS_names.append("%s/%s" % (path, name))
-    if not SoVITS_names:
-        SoVITS_names = [""]
-    GPT_names = []
-    for key in name2gpt_path:
-        if os.path.exists(name2gpt_path[key]):
-            GPT_names.append(key)
-    for path in GPT_weight_root:
-        if not os.path.exists(path):
-            continue
-        for name in os.listdir(path):
-            if name.endswith(".ckpt"):
-                GPT_names.append("%s/%s" % (path, name))
-    SoVITS_names = sorted(SoVITS_names, key=custom_sort_key)
-    GPT_names = sorted(GPT_names, key=custom_sort_key)
-    if not GPT_names:
-        GPT_names = [""]
-    return SoVITS_names, GPT_names
-
-
-def change_choices():
-    SoVITS_names, GPT_names = get_weights_names()
-    return {"choices": SoVITS_names, "__type__": "update"}, {
-        "choices": GPT_names,
-        "__type__": "update",
-    }
-
-
-# 推理用的指定模型
-sovits_path = ""
-gpt_path = ""
-is_half_str = os.environ.get("is_half", "True")
-is_half = True if is_half_str.lower() == "true" else False
-is_share_str = os.environ.get("is_share", "False")
-is_share = True if is_share_str.lower() == "true" else False
-
-cnhubert_path = "pretrained_models/chinese-hubert-base"
-bert_path = "pretrained_models/chinese-roberta-wwm-ext-large"
-pretrained_sovits_path = "pretrained_models/s2G488k.pth"
-pretrained_gpt_path = "pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt"
-
-exp_root = "logs"
-python_exec = sys.executable or "python"
-
-webui_port_main = 9874
-webui_port_uvr5 = 9873
-webui_port_infer_tts = 9872
-webui_port_subfix = 9871
-
-api_port = 9880
-
-
-# Thanks to the contribution of @Karasukaigan and @XXXXRT666
-def get_device_dtype_sm(idx: int) -> tuple[torch.device, torch.dtype, float, float]:
-    cpu = torch.device("cpu")
-    cuda = torch.device(f"cuda:{idx}")
-    if not torch.cuda.is_available():
-        return cpu, torch.float32, 0.0, 0.0
-    device_idx = idx
-    capability = torch.cuda.get_device_capability(device_idx)
-    name = torch.cuda.get_device_name(device_idx)
-    mem_bytes = torch.cuda.get_device_properties(device_idx).total_memory
-    mem_gb = mem_bytes / (1024**3) + 0.4
-    major, minor = capability
-    sm_version = major + minor / 10.0
-    is_16_series = bool(re.search(r"16\d{2}", name)) and sm_version == 7.5
-    if mem_gb < 4 or sm_version < 5.3:
-        return cpu, torch.float32, 0.0, 0.0
-    if sm_version == 6.1 or is_16_series == True:
-        return cuda, torch.float32, sm_version, mem_gb
-    if sm_version > 6.1:
-        return cuda, torch.float16, sm_version, mem_gb
-    return cpu, torch.float32, 0.0, 0.0
-
-
-IS_GPU = True
-GPU_INFOS: list[str] = []
-GPU_INDEX: set[int] = set()
-GPU_COUNT = torch.cuda.device_count()
-CPU_INFO: str = "0\tCPU " + i18n("CPU训练,较慢")
-tmp: list[tuple[torch.device, torch.dtype, float, float]] = []
-memset: set[float] = set()
-
-for i in range(max(GPU_COUNT, 1)):
-    tmp.append(get_device_dtype_sm(i))
-
-for j in tmp:
-    device = j[0]
-    memset.add(j[3])
-    if device.type != "cpu":
-        GPU_INFOS.append(f"{device.index}\t{torch.cuda.get_device_name(device.index)}")
-        GPU_INDEX.add(device.index)
-
-if not GPU_INFOS:
-    IS_GPU = False
-    GPU_INFOS.append(CPU_INFO)
-    GPU_INDEX.add(0)
-
-infer_device = max(tmp, key=lambda x: (x[2], x[3]))[0]
-is_half = any(dtype == torch.float16 for _, dtype, _, _ in tmp)
-
-
-class Config:
-    def __init__(self):
-        self.sovits_path = sovits_path
-        self.gpt_path = gpt_path
-        self.is_half = is_half
-
-        self.cnhubert_path = cnhubert_path
-        self.bert_path = bert_path
-        self.pretrained_sovits_path = pretrained_sovits_path
-        self.pretrained_gpt_path = pretrained_gpt_path
-
-        self.exp_root = exp_root
-        self.python_exec = python_exec
-        self.infer_device = infer_device
-
-        self.webui_port_main = webui_port_main
-        self.webui_port_uvr5 = webui_port_uvr5
-        self.webui_port_infer_tts = webui_port_infer_tts
-        self.webui_port_subfix = webui_port_subfix
-
-        self.api_port = api_port
+import os
+import re
+import sys
+
+import torch
+
+from tools.i18n.i18n import I18nAuto
+
+i18n = I18nAuto(language=os.environ.get("language", "Auto"))
+
+
+pretrained_sovits_name = {
+    "v1": "pretrained_models/s2G488k.pth",
+    "v2": "pretrained_models/gsv-v2final-pretrained/s2G2333k.pth",
+    "v3": "pretrained_models/s2Gv3.pth",  ###v3v4还要检查vocoder，算了。。。
+    "v4": "pretrained_models/gsv-v4-pretrained/s2Gv4.pth",
+    "v2Pro": "pretrained_models/v2Pro/s2Gv2Pro.pth",
+    "v2ProPlus": "pretrained_models/v2Pro/s2Gv2ProPlus.pth",
+}
+
+pretrained_gpt_name = {
+    "v1": "pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt",
+    "v2": "pretrained_models/gsv-v2final-pretrained/s1bert25hz-5kh-longer-epoch=12-step=369668.ckpt",
+    "v3": "pretrained_models/s1v3.ckpt",
+    "v4": "pretrained_models/s1v3.ckpt",
+    "v2Pro": "pretrained_models/s1v3.ckpt",
+    "v2ProPlus": "pretrained_models/s1v3.ckpt",
+}
+name2sovits_path = {
+    # i18n("不训练直接推v1底模！"): "pretrained_models/s2G488k.pth",
+    # i18n("不训练直接推v2底模！"): "pretrained_models/gsv-v2final-pretrained/s2G2333k.pth",
+    # i18n("不训练直接推v3底模！"): "pretrained_models/s2Gv3.pth",
+    # i18n("不训练直接推v4底模！"): "pretrained_models/gsv-v4-pretrained/s2Gv4.pth",
+    # i18n("不训练直接推v2Pro底模！"): "pretrained_models/v2Pro/s2Gv2Pro.pth",
+    # i18n("不训练直接推v2ProPlus底模！"): "pretrained_models/v2Pro/s2Gv2ProPlus.pth",
+}
+name2gpt_path = {
+    # i18n("不训练直接推v1底模！"):"pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt",
+    # i18n(
+    #     "不训练直接推v2底模！"
+    # ): "pretrained_models/gsv-v2final-pretrained/s1bert25hz-5kh-longer-epoch=12-step=369668.ckpt",
+    # i18n("不训练直接推v3底模！"): "pretrained_models/s1v3.ckpt",
+}
+SoVITS_weight_root = [
+    "SoVITS_weights",
+    "SoVITS_weights_v2",
+    "SoVITS_weights_v3",
+    "SoVITS_weights_v4",
+    "SoVITS_weights_v2Pro",
+    "SoVITS_weights_v2ProPlus",
+]
+GPT_weight_root = [
+    "GPT_weights",
+    "GPT_weights_v2",
+    "GPT_weights_v3",
+    "GPT_weights_v4",
+    "GPT_weights_v2Pro",
+    "GPT_weights_v2ProPlus",
+]
+SoVITS_weight_version2root = {
+    "v1": "SoVITS_weights",
+    "v2": "SoVITS_weights_v2",
+    "v3": "SoVITS_weights_v3",
+    "v4": "SoVITS_weights_v4",
+    "v2Pro": "SoVITS_weights_v2Pro",
+    "v2ProPlus": "SoVITS_weights_v2ProPlus",
+}
+GPT_weight_version2root = {
+    "v1": "GPT_weights",
+    "v2": "GPT_weights_v2",
+    "v3": "GPT_weights_v3",
+    "v4": "GPT_weights_v4",
+    "v2Pro": "GPT_weights_v2Pro",
+    "v2ProPlus": "GPT_weights_v2ProPlus",
+}
+
+
+def custom_sort_key(s):
+    # 使用正则表达式提取字符串中的数字部分和非数字部分
+    parts = re.split("(\d+)", s)
+    # 将数字部分转换为整数，非数字部分保持不变
+    parts = [int(part) if part.isdigit() else part for part in parts]
+    return parts
+
+
+def get_weights_names():
+    SoVITS_names = []
+    for key in name2sovits_path:
+        if os.path.exists(name2sovits_path[key]):
+            SoVITS_names.append(key)
+    for path in SoVITS_weight_root:
+        if not os.path.exists(path):
+            continue
+        for name in os.listdir(path):
+            if name.endswith(".pth"):
+                SoVITS_names.append("%s/%s" % (path, name))
+    if not SoVITS_names:
+        SoVITS_names = [""]
+    GPT_names = []
+    for key in name2gpt_path:
+        if os.path.exists(name2gpt_path[key]):
+            GPT_names.append(key)
+    for path in GPT_weight_root:
+        if not os.path.exists(path):
+            continue
+        for name in os.listdir(path):
+            if name.endswith(".ckpt"):
+                GPT_names.append("%s/%s" % (path, name))
+    SoVITS_names = sorted(SoVITS_names, key=custom_sort_key)
+    GPT_names = sorted(GPT_names, key=custom_sort_key)
+    if not GPT_names:
+        GPT_names = [""]
+    return SoVITS_names, GPT_names
+
+
+def change_choices():
+    SoVITS_names, GPT_names = get_weights_names()
+    return {"choices": SoVITS_names, "__type__": "update"}, {
+        "choices": GPT_names,
+        "__type__": "update",
+    }
+
+
+# 推理用的指定模型
+sovits_path = ""
+gpt_path = ""
+is_half_str = os.environ.get("is_half", "True")
+is_half = True if is_half_str.lower() == "true" else False
+is_share_str = os.environ.get("is_share", "False")
+is_share = True if is_share_str.lower() == "true" else False
+
+cnhubert_path = "pretrained_models/chinese-hubert-base"
+bert_path = "pretrained_models/chinese-roberta-wwm-ext-large"
+pretrained_sovits_path = "pretrained_models/s2G488k.pth"
+pretrained_gpt_path = "pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt"
+
+exp_root = "logs"
+python_exec = sys.executable or "python"
+
+webui_port_main = 9874
+webui_port_uvr5 = 9873
+webui_port_infer_tts = 9872
+webui_port_subfix = 9871
+
+api_port = 9880
+
+
+# Thanks to the contribution of @Karasukaigan and @XXXXRT666
+def get_device_dtype_sm(idx: int) -> tuple[torch.device, torch.dtype, float, float]:
+    cpu = torch.device("cpu")
+    cuda = torch.device(f"cuda:{idx}")
+    if not torch.cuda.is_available():
+        return cpu, torch.float32, 0.0, 0.0
+    device_idx = idx
+    capability = torch.cuda.get_device_capability(device_idx)
+    name = torch.cuda.get_device_name(device_idx)
+    mem_bytes = torch.cuda.get_device_properties(device_idx).total_memory
+    mem_gb = mem_bytes / (1024**3) + 0.4
+    major, minor = capability
+    sm_version = major + minor / 10.0
+    is_16_series = bool(re.search(r"16\d{2}", name)) and sm_version == 7.5
+    if mem_gb < 4 or sm_version < 5.3:
+        return cpu, torch.float32, 0.0, 0.0
+    if sm_version == 6.1 or is_16_series == True:
+        return cuda, torch.float32, sm_version, mem_gb
+    if sm_version > 6.1:
+        return cuda, torch.float16, sm_version, mem_gb
+    return cpu, torch.float32, 0.0, 0.0
+
+
+IS_GPU = True
+GPU_INFOS: list[str] = []
+GPU_INDEX: set[int] = set()
+GPU_COUNT = torch.cuda.device_count()
+CPU_INFO: str = "0\tCPU " + i18n("CPU训练,较慢")
+tmp: list[tuple[torch.device, torch.dtype, float, float]] = []
+memset: set[float] = set()
+
+for i in range(max(GPU_COUNT, 1)):
+    tmp.append(get_device_dtype_sm(i))
+
+for j in tmp:
+    device = j[0]
+    memset.add(j[3])
+    if device.type != "cpu":
+        GPU_INFOS.append(f"{device.index}\t{torch.cuda.get_device_name(device.index)}")
+        GPU_INDEX.add(device.index)
+
+if not GPU_INFOS:
+    IS_GPU = False
+    GPU_INFOS.append(CPU_INFO)
+    GPU_INDEX.add(0)
+
+infer_device = max(tmp, key=lambda x: (x[2], x[3]))[0]
+is_half = any(dtype == torch.float16 for _, dtype, _, _ in tmp)
+
+
+class Config:
+    def __init__(self):
+        self.sovits_path = sovits_path
+        self.gpt_path = gpt_path
+        self.is_half = is_half
+
+        self.cnhubert_path = cnhubert_path
+        self.bert_path = bert_path
+        self.pretrained_sovits_path = pretrained_sovits_path
+        self.pretrained_gpt_path = pretrained_gpt_path
+
+        self.exp_root = exp_root
+        self.python_exec = python_exec
+        self.infer_device = infer_device
+
+        self.webui_port_main = webui_port_main
+        self.webui_port_uvr5 = webui_port_uvr5
+        self.webui_port_infer_tts = webui_port_infer_tts
+        self.webui_port_subfix = webui_port_subfix
+
+        self.api_port = api_port